The objective of this study was to analyze land use/land cover (LULC) changes in the landscape of Munessa-Shashemene area of the Ethiopian highlands over a period of 39 years (1973–2012). Satellite images of Landsat MSS (1973), TM (1986), ETM+ (2000), and RapidEye (2012) were used. All images were classified using object-based image classification technique. Accuracy assessments were conducted for each reference year. Change analysis was carried out using post classification comparison in GIS. Nine LULCs were successfully captured with overall accuracies ranging from 85.7% to 93.2% and Kappa statistic of 0.822 to 0.924. The classification result revealed that grasslands (42.3%), natural forests (21%), and woodlands (11.4%) were dominant LULC types in 1973. In 2012, croplands (48.5%) were the major LULC types followed by others. The change result shows that a rapid reduction in woodland cover of 81.8%, 52.3%, and 36.1% occurred between the first (1973–1986), second (1986–2000), and third (2000–2012) study periods, respectively. Similarly, natural forests cover decreased by 26.1% during the first, 21.1% during the second, and 24.4% during the third periods. Grasslands also declined by 11.9, 17.5, and 21.1% during the three periods, respectively. On the contrary, croplands increased in all three periods by 131, 31.5, and 22.7%, respectively. Analysis of the 39-year change matrix revealed that about 60% of the land showed changes in LULC. Changes were also common along the slope gradient and agro-ecological zones with varying proportions. Further study is suggested to investigate detailed drivers and consequences of changes.

Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate data source for DSM generation. In order to fill this gap, this paper performs change detection analysis including forest decrease and tree growth. The accuracy of the DSMs is evaluated by comparison with measured tree heights from inventory plots (field data). In addition, the DSMs are compared with LiDAR data to perform a pixel-wise quality assessment. DSMs from four different satellite stereo sensors (ALOS/PRISM, Cartosat-1, RapidEye and WorldView-2), one satellite InSAR sensor (TanDEM-X), two aerial stereo camera systems (HRSC and UltraCam) and two airborne laser scanning datasets with different point densities are adopted for the comparison. The case study is a complex central European temperate forest close to Traunstein in Bavaria, Germany. As a major experimental result, the quality of the DSM is found to be robust to variations in image resolution, especially when the forest density is high. The forest decrease results confirm that besides aerial photogrammetry data, very high resolution satellite data, such as WorldView-2, can deliver results with comparable quality as the ones derived from LiDAR, followed by TanDEM-X and Cartosat DSMs. The quality of the DSMs derived from ALOS and Rapid-Eye data is lower, but the main changes are still correctly highlighted. Moreover, the vertical tree growth and their relationship with tree height are analyzed. The major tree height in the study site is between 15 and 30 m and the periodic annual increments (PAIs) are in the range of 0.30–0.50 m.

Monitoring urban growth patterns is an important measure to improve our understanding of land use/land cover (LULC) changes and a central part in the proper development of any city. In this study, we analyzed the changes over a period of 30 years (1985–2015) in Bahir Dar, one of the rapidly growing cities of northwest Ethiopia. Satellite images of Landsat TM (1985, 1995, and 2008), and OLI (2015) were used. The classification was carried out using the object-based image analysis technique and a change analysis was undertaken using post-classification comparison in GIS as a novel framework. An accuracy assessment was conducted for each reference year. Eight LULC types were successfully captured with overall accuracies ranging from 88.3% to 92.9% and a Kappa statistic of 0.85 to 0.92. The classification result revealed that cropland (66%), water (12.5%), and grassland (6%) were the dominant LULC types with a small share of areas covered by built-up areas (2.4%) in 1985. In 2015, cropland and water continued to be dominant followed by built-up areas. The change result shows that a rapid reduction in natural forest cover followed by grassland and wetland occurred between the first (1985–1995), second (1995–2008), and third (2008–2015) study periods. On the contrary, build-ups increased in all three periods by 9.3%, 121.3%, and 44.8%, respectively. Although the conversion between the LULC classes varied substantially, analysis of the 30-year change matrix revealed that about 31% was subject to intensive change between the classes. Specifically, the built-up area has increased by 250.5% during the study years. The framed approach used in this research is a good repeatable example of how to assess and monitor urban growth at the local level, by combining remote sensing and GIS technologies. Further study is suggested to investigate detailed drivers, consequences of changes, and future options.

Submersed aquatic vegetation (SAV) is sensitive to changes in environmental conditions and plays an important role as a long-term indictor for the trophic state of freshwater lakes. Variations in water level height, nutrient condition, light availability and water temperature affect the growth and species composition of SAV. Detailed information about seasonal variations in littoral bottom coverage are still unknown, although these effects are expected to mask climate change-related long-term changes, as derived by snapshots of standard monitoring methods included in the European Water Framework Directive. Remote sensing offers concepts to map SAV quickly, within large areas, and at short intervals. This study analyses the potential of a semi-empirical method to map littoral bottom coverage by a multi-seasonal approach. Depth-invariant indices were calculated for four Atmospheric & Topographic Correction (ATCOR2) atmospheric corrected RapidEye data sets acquired at Lake Kummerow, Germany, between June and August 2015. RapidEye data evaluation was supported by in situ measurements of the diffuse attenuation coefficient of the water column and bottom reflectance. The processing chain was able to differentiate between SAV and sandy sediment. The successive increase of SAV coverage from June to August was correctly monitored. Comparisons with in situ and Google Earth imagery revealed medium accuracies (kappa coefficient = 0.61, overall accuracy = 72.2%). The analysed time series further revealed how water constituents and temporary surface phenomena such as sun glint or algal blooms influence the identification success of lake bottom substrates. An abundant algal bloom biased the interpretability of shallow water substrate such that a differentiation of sediments and SAV patches failed completely. Despite the documented limitations, mapping of SAV using RapidEye seems possible, even in eutrophic lakes.